When performing manual actions, eye movements precede hand movements to target locations: Before we grasp an object, we look at it. Eye-hand guidance is even preserved when visual targets are unavailable, e.g., grasping behind an occlusion. This “looking-atnothing” behavior might be functional, e.g., as “deictic pointer” for manual control or as memory-retrieval cue, or a by-product of automatization. Here, it is studied if looking at empty locations before acting on them is beneficial for sensorimotor performance. In five experiments, participants completed a click sequence on eight visual targets for 0-100 trials while they had either to fixate on the screen center or could move their eyes freely. During 50-100 consecutive trials, participants clicked the same sequence on a blank screen with free or fixed gaze. During both phases, participants looked at target locations when gaze shifts were allowed. With visual targets, target fixations led to faster, more precise clicking, fewer errors, and sparser cursor-paths than central fixation. Without visual information, a tiny free-gaze benefit could sometimes be observed and was rather a memory than a motor-calculation benefit. Interestingly, central fixation during learning forced early explicit encoding causing a strong benefit for acting on remembered targets later, independent of whether eyes moved then. When performing manual actions, such as driving, writing, or grasping anobject, we use our eyes to guide our hands to target locations in theenvironment , 7, 8, 9By which mechanism could fixating a target location facilitatehand-movement programming in the absence of visual targets? A singlestep of a manual action typically consists of a first covert shift ofattention, a subsequent eye movement, and a final hand movement to and amanipulation at the action-target location. With visual information,target features such as shape or size can be extracted by a targetfixation and used for motor calculation. Without visual information, thefixation point can still be used to locate the remembered target forhand movement calculation in external three-dimensional space, e.g., bycalculating the remembered distance of a target to the hand on the basisof the currently fixated point in the world. Neurons that are tuned tozero-disparity at the fovea can be used in the process to connect theinternal space of the eyeball position in the head to the externalthree-dimensional space of the fixation position in the world (38). Theidea is that this leads in turn to a fast access of the rememberedtarget location in external space  and should facilitate hand-movement programming . InaddiAlthough it is plausible to assume that looking-at-nothing isfunctional for sensorimotor control on the basis of the aforementionedreasons, no study has justified any of these assumptions so far. Thefact that looking-at-nothing has been found in sensorimotor tasks , 22, 47 In sum, there are several possible functions of looking-at-nothing ingeneral as well as specifically for sensorimotor tasks which need to betested. The present study aimed at clarifying which function looking atinvisible action-target locations might fulfill. In five experiments,participants performed a computerized adaptation of the numberconnection test or trail making test, version A , 50, 51.Participation in all reported experiments followed provision ofwritten informed consent. All experiments were approved by the Committeefor Ethics at Bielefeld University (EUB) and performed in accordancewith the approved guidelines. All participants reported normal visualacuity or were tested with correcting lenses and were recruited atBielefeld University, Germany. All were naïve with respect to thepurpose of the respective study and were paid for their participation.Forty right-handed students  with a mean age of24 years completed Experiment 1. Two additional participants did notcomplete the experiment and did therefore not enter the analyses.Experimentation took place in a dimly lit room. The experiment wascontrolled by the Experiment Builder software  on a Dell Optiplex 755 computer. The stimuli were displayed on a19-inch color CRT monitor (ViewSonic Graphics Series G90fB using an ATIRadeon HD 2400 Pro graphics card) with a refresh rate of 100 Hz and aresolution of 1,024 x 768 pixels extending to 36 x 27 cm. The computermouse and keyboard as well as an extra-large mouse pad (88 x 32 cm) wereused. Each participant’s right gaze position was recorded with 1,000 Hzby an EyeLink 1000 tower system .Participants’ viewing distance was fixed at 71 cm by the system’s chinand forehead rest throughout the experiment. Color and luminance weremeasured in CIE Lxy coordinates using an X-Rite i1 Pro spectrophotometer.2, x = .29, y = .30). A black plus  of 0.43 degrees of visual angle(°v.a.) in width and height was located in the center of the screen andserved as central fixation cross in case of restricted eye movements. Ablack dot  with a diameterof approximately 0.43°v.a. constituted the mouse cursor. Eight circulartarget areas with a diameter of 3.06°v.a. were defined. Their spatialconfiguration was randomly generated with the prerequisite that eachouter field of an imagined 3x3 grid contained one target area, andtarget areas had a minimal distance of 2.04°v.a. to each other(border-to-border) as well as to the screen border. In the visual-targettrials, each target region contained one of eight black numbers in itscenter . Each number wassurrounded by an unfilled black circle . The target configuration was the same throughout the entireexperiment. The stimuli layout can be seen in Figure 1 (upper left).The computer screen showed a gray background . Participants were informed that theconfiguration of target stimuli stayed the same throughout the wholeexperiment, so that they could learn the location sequence. In theblank-screen recall phase, only the dot cursor and the central fixationplus were displayed on the grey background  andparticipants were instructed to click as fast as possible in the samesequence on the locations which had been occupied by a numbered circleduring the visual-acquisition phase. Both phases were preceded by anexample trial that was not included in the analysis.A click was counted as correct within the circular target area(diameter of 3.06°v.a.). A correct click was followed by a high-pitchedtone. After all eight target regions were clicked on in the correctorder trial-completion time was displayed on the screen. Each trial waspreceded by a central fixation on a black ring (0.45°v.a. outer size and0.11°v.a. inner size) for calibration check. Calibration and validationwere repeated if necessary.The visual-learning phase consisted of 100 trials and theblank-screen recall phase consisted of 50 trials, both performed inblocks of 10 trials. A block information display separated each block.Participants could start each block and trial by pressing the space bar.Participants were allowed to take self-paced breaks in-between blocksand trials.Half of the participants were instructed to keep fixation on thecentral plus throughout each visual-learning trial (fix-learning group),while the other half did not receive any instruction concerning theireye movements for the visual-learning phase (free-learning group).During the blank-screen recall phase half of the participants of eachlearning group had to keep central fixation throughout each trial, while the other half could use their eyes freely, resulting in two recall groups with counterbalancedlearning conditions . The fixationmanipulation during the visual-learning phase was included for tworeasons. On the one hand, it enables to verify whether foveal visionbenefits acting on visual targets also in the task applied here ‑ aprerequisite for investigating whether the same task also benefits fromlooking at empty target locations. On the other hand, performance duringrecall might depend on how the task was learned. It is known thatinformation can be retrieved best when the circumstances are keptconstant . Besides the totalnumber of errors, two error types were analyzed. Incorrect clicks thatwere less than three circle radii distant from the center of the currentsequence target were defined as precision errors. Incorrect clicks thatwere less than three circle radii distant from any other item weredefined as sequence errors.t-tests) were calculated to compare experimental groupsper block. The overall result pattern can best be grasped from theresult figures.Mixed design analyses of variance (ANOVAs) with groups  as between-subject factors and block(1-10 and 11-15) as within-subject factor were calculated per dependentvariable and experimental phase  to reveal whether thepossibility to move the eyes freely benefitted performance. Theresulting values are reported in tables. In case of significantinteractions, further analyses . The recording samples of the last nine trials of subject16  and the last 27 trials of subject 37 were not written into the raw data filedue to space problems. Therefore, these trials are missing in all gazeanalyses as well as in the error type analyses.SR Research’s default velocity algorithm was used to detect fixations and Microsoft Excel 2010. All statistical analyses were performedusing R3.4.0  and the ε. The uncorrected degrees of freedom are reportedtogether with the Greenhouse-Geisser ε. In case ofWelch correction for t-tests, the corrected degrees offreedom are reported. In case of violation of a normal distributionaccording to the Kolmogorov-Smirnoff test, results were validated withnon-parametric tests where appropriate. Only deviating non-parametricresults are reported. A chance level of .05 was applied unless reportedotherwise.Violations of sphericity were corrected using the Greenhouse-GeisserOn average 0.5 trials were abandoned before a trial was completed inthe central-fixation group due to fixation disengagement. This is aratio of one abandoned to two completed trials. In other words,participants completed on average two of three trials. The free-learninggroup performed on average 6.8 guiding fixations, indicating thatparticipants who were allowed to move their eyes fixated most of theeight targets before clicking on them.The learning group x block ANOVA for trial completion time revealedsignificant main effects of group and block, as well as a significantinteraction (Table 1). Independent sample t-tests revealed that thefree-gaze group was significantly faster than the fixation groupthroughout all visual blocks (ps < 0.05), with more pronounceddifference early during learning . The block effect was due todecreasing trial completion time over the course of learning for bothgroups (linear trend ps < .001).2(1) = 10, p < .01). Wilcoxon tests indicatedsignificantly less precision errors performed by the fix-gaze group thanthe free-gaze group during blocks 5 and 8-10 (ps < .05). The blockeffect was due to an increasing distance from the target center oferroneous clicks in the range of the target for the fix-gaze group(linear trend p < 0.001), but not for the free-gaze group (lineartrend p = 0.23).The analysis of all errors revealed a significant main effect ofgroup only (Table 1) with a better average performance of the free-gazegroup compared to the fixation group . The analysis of errorsclassified as sequence errors did not reveal any significant effect(Table 1) as participants mostly did not perform any sequence errorsduring their completed trials. The analysis of errors classified asprecision errors did only reveal a significant block effect (Table 1) aswell as a group effect according to non-parametric testing . Cursor-pathlengths decreased (linear trend p < .001), while clicking became lesscentered on the targets (linear trend p < .001) over the course ofthe learning phase.p < .01),while the group that learned already with central fixation showed only atrend towards further improvement (linear trend p =.11). The significant block x group interaction was due to the fact thatthe two learning groups differed significantly in their ability to keepcentral fixation only during the first recall block (Block 11:t(13.99) = 2.59, zCohen’s d = 1.16, p < .05; Figure 3). Nevertheless, thisresult indicates how important it is to have both learning groupsincluded to compare performance with gaze restriction againstperformance with free gaze when acting on an empty screen. A differencebetween free- and fix- gaze participants that learned with free gazecould likely be due to the fact that the central-fixation group has tolearn to keep this central fixation successfully. Comparing theperformance of free-recall and fix-recall participants who all learnedwith central fixation lacks this confound. However, as learning andrecall situation is equal only for the fix-learning and fix-recallgroup, the free-learning and free-recall groups are also required. By acompletely matched design, the effects of learning group, recall group,as well as their interaction can be revealed.On average 0.7 trials were abandoned before a trial was completedwhen central fixation was required. Did the frequency of fixationdisengagements depend on whether participants had learned with centralfixation or free gaze? A mixed-design ANOVA with block (11-15) aswithin-subject factor and learning group (free vs. fix) asbetween-subject factor was performed on those 20 participants who had tokeep central fixation throughout each recall trial. The analysisrevealed a significant block effect, a significant block x groupinteraction, but no group effect (Table 2). The group that learned withfree gaze improved significantly in keeping central fixation over thecourse of the recall phase (linear trend ps <.05). Independent sample t-tests did not showsignificant group differences in the number of guiding fixations in anyof the five recall blocks (but a trend in Block 11 withp = 0.08). Thus, participants scanned the empty targetlocations sequentially, when they were allowed to move their eyesfreely, independently of how they learned the sequence and more so inthe beginning of the recall phase.Averaged over all blocks, free-gaze participants performed 4.5guiding fixations per recall trial. Did those participants, who wereallowed to use their eyes freely, scan the targets similarlyirrespective of how they learned the sequence (free vs. fix)? Amixed-design ANOVA for the number of guiding fixations with learninggroup (free vs. fix) as between-subject factor and blocks (11-15) aswithin-subject factor was performed on those 20 participants who wereallowed to move their eyes freely during recall. The analysis revealed asignificant block effect . The block effect was dueto a decreasing number of guiding fixations over the course of therecall blocks for both groups  with block aswithin-subject factor (11-15), and learning group (free vs. fix) andrecall group (free vs. fix) as between-subject factors. The ANOVA forclick precision did not reveal any significant effects (Table 2). TheANOVAs for the other performance measures revealed a significant blockeffect as well as significant block x group interactions, both withlearning group and with recall group (except for precision errors thatshowed only a trend), but neither a learning group x recall group, nor athree-way interaction (Table 2).F = 4.47,2η  = .11, p < .05), all errors  = 4.80, 2η  = .12, p < .05), sequence errors  = 6.49, 2η  = .16, p < .05), and path  = 5.88, 2η  = .14, p < .05) due to better performance of those participants who learned with central fixation , independent of whether they continued fixating during recall or were allowed to move their eyes. Nolearning group x recall group interaction reached significance for anyof the performance measures in any block. For trial completion time andsequence errors, the main effect of recall group reached significance inBlock 11  = 4.90, 2η  = .12, p < .05; sequence errors: F = 4.86, 2η  = .13, p < .05, but Friedman 2Χ(3) = 50, p = .27) with faster completion times of those participants who had to keep central fixation (18.60 s vs. 25.46 s), but eventually more sequence errors included (12.63 vs. 11.84).Between-subject ANOVAs with learning and recall group per blockrevealed that the block interactions were due to the fact that groupsdiffered mainly during the first recall block (Block 11). Specifically,only in Block 11, there was a significant main effect of learning groupin time .Only the group that had to keep central fixation throughout theexperiment (FixFix) did not improve much over the course of the recallphase, presumably because this group already started the recall phasewith near-asymptote performance .The block effects of the three-way ANOVA were due to improvingperformance measures over the course of the recall phase for most of thegroups, indicated by linear trends with a mean age of 23 years completed Experiment 2. The data of threeadditional participants were incomplete and did therefore not enter theanalyses.Apparatus and stimuli were the same as in Experiment 1, only that anew spatial configuration of eight target regions was generated forExperiment 2  with the same prerequisites as inExperiment 1. This spatial configuration of target regions was the samethroughout the entire experiment.The procedure was the same as in Experiment 1 except for fixationdisengagement handling. When eye movements were restricted and a gazesample was detected outside of a diameter of 3.06°v.a. around thecentral plus, the trial was halted. Specifically, a deep tone sounded,the fixation plus turned red, clicks did no longer count, and the cursorwas extinguished as were the numbered circles in case of the visualphase. As soon as a gaze sample was detected within the central fixationregion again, the trial continued. Specifically, the fixation plusturned black again, clicks counted again, the cursor reappeared as wellas the numbered circles in case of the visual phase. In this way, thenumber of trials that were started and completed was exactly the samefor all participants and participants were exposed to the visualmaterial only while they obeyed the instruction.Analyses were the same as in Experiment 1.Central-fixation participants looked on average 1.5 times per trialoutside of the central-fixation region. Participants who were allowed tomove their eyes freely executed on average 7.0 guiding fixations,indicating that they scanned most of the eight targets beforeclicking.Mixed measures ANOVAs with learning group (free vs. fix) asbetween-subject and block (1-10) as within-subject factor for all fourperformance measures were conducted to reveal possible benefits ofmoving the eyes freely.The analysis of trial completion time mirrored the results ofExperiment 1 with significant block and group main effects as well as asignificant interaction (Table 3).Also, the analysis of all types of errors resulted in significantmain effects of block and group as well as a significant interactionthis time as did the analysis of cursor-path length (Table 3).p < 001) as well as a trend towards aninteraction.Identical to the results of Experiment 1, the ANOVA of clickprecision yielded only a significant block main effect (Table 3) due toincreasingly less central clicking on the targets , with more pronounced differences early during learning .Independent sample ps < .05 fornumber of errors and ps < .001 for all others)except for the number of sequence errors of the free-gaze group (lineartrend p = .13), which did not perform sequence errorsfrom the beginning. Note, that in contrast to Experiment 1, the fix-gazeparticipants performed initially a high number of sequence errors in thepresent study. It is likely that the true number of sequence errors inExperiment 1 was covered due to the procedure of abandoning trials afterfixation disengagement that did not enter the analysis.Most performance measures improved significantly over the course oflearning for both groups (linear trend p = .05).During the recall phase, on average 0.6 disengaged fixations wereobserved per trial when participants were instructed to keep centralfixation. A mixed ANOVA with block (11-15) as within-subject factor andlearning group (free vs. fix) as between-subject factor revealed only asignificant main effect of block, but not of group, nor a significantinteraction . Thus, the number of disengagedfixations did not differ significantly between the participants wholearned with fix vs. free gaze in this experiment with an equal numberof started and completed learning trials. The block effect was due to atrend towards improvement in keeping central fixation over the course ofthe recall phase (linear trend t(17.95) = 2.76,zCohen’s d = 1.23, p <.05; Figure 5). In addition, only the free-learning group reduced theamount of guiding fixations over the course of the recall phase (lineartrend p < .05 for free vs. p = 0.55for fix learning) and in this way approached the fix-learning group’slevel.Free-gaze participants performed on average 4 guiding fixations perrecall trial. A learning group (free vs. fix) by block (11-15) ANOVArevealed a significant block effect and trends towards a group effectand towards a block x group interaction (Table 4). The interaction trendwas due to the fact that the learning groups differed significantly inthe number of their guiding fixations during the first recall block, butno longer thereafter  and learning and recall group (free vs.fix) as between-subject factors. Mirroring the results of Experiment 1,the analysis of click precision did not reveal any significant effects(Table 4), however this time with a trend for the block x learning groupinteraction. The analyses for the other performance measures revealedagain significant block effects and block x learning group interactionsor a trend towards it in case of precision errors. However, there was nosignificant effect of recall group, nor any significant interaction withit (Table 4). The learning group effect showed a trend towardssignificance only when considering the sequence errors. The three-wayinteraction reached significance only for the number of precisionerrors.F = 4.28,2η = .11, p < .05; allerrors: F = 11.40,2η = .24, p < .01;sequence errors: F = 8.16,2η = .18, p < .01;precision errors: F = 4.67,2η = .11, p < .05; path:F = 7.22, 2η = .17,p < .05) due to better performance of theparticipants that learned with central fixation . Thus, again participants who had learned with central fixationoutperformed participants who had learned with free gaze during thefirst recall block, and that was the case independent of whether or notparticipants moved their eyes during recall. No learning group x recallgroup interaction reached significance for any of the performancemeasures in any block, neither did any recall group main effect.Similarly to Experiment 1, between-subject ANOVAs with learning andrecall group per block revealed that the block interactions were due tothe fact that groups differed significantly only during the first recallblock (Block 11). Specifically, there was a main effect of learninggroup in all five variables .The block effects of the three-way ANOVAs were mostly due toperformance improvements over the course of the recall phase of bothlearning groups, indicated by linear trends . Again, looking-at-nothing was notaccompanied by any performance benefit. Although the learning groups nowstarted and completed the same amount of trials, participants wholearned the task with central fixation during the visual phase stilloutperformed those participants who learned with free gaze when it cameto clicking the learned sequence on a blank-screen (Block 11).Specifically, fix-learning participants completed the trials faster withfewer errors of all types and shorter cursor-paths. Importantly, this“fixation-learning” benefit was observed not only for participants whohad to continue central fixation, but also for participants who wereallowed to move their eyes during recall. The source of this“fixation-learning” benefit will be investigated in Experiment 4. This“fixation-learning” benefit might have covered a potential free-gazebenefit in the blank blocks. Experiment 3 was conducted to investigatewhether the recall groups might start to differ at the end of an evenlonger blank-screen phase, when the “fixation-learning” benefit might nolonger exert any coverings.In Experiment 3, the blank-screen phase was extended to 10 blocks of10 trials each. A new sample of 40 participants was recruited andparticipants were assigned to a central-fixation and a free-gaze groupin the same way as in the previous experiments. Experiment 3 testedwhether a benefit of looking-at-nothing can be observed after the“fixation-learning” benefit has completely washed out over the course ofa long blank-screen recall phase.A new sample of forty right-handed students with a mean age of 25 years completed Experiment 3. The data of twoadditional participants were incomplete and did therefore not enter theanalyses.Apparatus and stimuli were the same as in Experiment 2, including thespatial configuration of the eight target regions .The procedure was the same as in Experiment 2, except that the recallphase was extended from 5 to 10 blocks à 10 trials. In this way, it canbe measured whether a free-gaze benefit shows up late during theblank-screen recall phase, when the benefit of having learned theconfiguration with central fixation might have already completelyceased.Analyses were the same as in the previous experiments.Participants who were asked to keep central fixation performed onaverage 1.6 fixations outside the fixation region. On average 8.2guiding fixations were executed by participants who were allowed to movetheir eyes freely, indicating sequential target scanning.Again, mixed measures ANOVAs (learning group x block) were conductedfor all performance measures. Mirroring the results of Experiment 2, theanalysis of trial completion time, number of all error types, andcursor-path length resulted in significant block and group main effectsas well as significant interactions (Table 5). The analysis of clickprecision revealed besides a significant block effect also a significantgroup effect this time, and a trend towards an interaction (Table5).ps < 0.05, exceptfor sequence errors in block 9 with p = 0.06) asconfirmed by significant independent sample t-tests.Significantly shorter cursor-paths of the fix-gaze participants wereobserved in the first five blocks (ps < 0.05). Thistime, also click precision of the free-gaze participants was higher insome blocks . Differences were most pronounced early during the learning phase.Again, free-gaze participants completed a trial significantly fasterwith fewer errors of all types , while click precision decreased (linear trendps < .001).Replicating the results of Experiments 1 and 2, time, errors, andcursor-path length improved significantly over the course of learning.During the recall phase, on average 0.5 fixations were detectedoutside the fixation region per trial and fix-gaze participant. A mixedANOVA (learning group x block) revealed a significant block effect and asignificant block x group interaction . Theeffects were due to the fact that disengagements decreased more stronglyduring recall for the group that did not already learn with centralfixation (linear trend of p = .11 for thefree-learning group and p = .17 for the fix-learninggroup). Learning groups differed significantly during blocks 14(t(16.37) = 2.56, zCohen’sd = 1.14, p < .05), 16(t(17.77) = 2.22, zCohen’sd = .99, p < .05), and 18(t(17.61) = 2.12, zCohen’sd = .95, p < .05) with lessguiding fixations of the group that learned with central fixation.Free-gaze participants performed on average 5.2 guiding fixations perblank-screen recall trial. The mixed ANOVA (learning group x block)revealed a significant block effect, a significant group effect, but nosignificant interaction (Table 6). Linear trend analysis showedeventually tendencies towards a decreasing number of guiding fixationsin both groups  = 5.00,2η = .06, p < .001) dueto a slight further decrease in click precision over the course of therecall phase .Mirroring the results of Experiment 2, the other performance measuresall revealed a significant block effect, a significant learning groupeffect or at least a trend (precision errors), and a significantlearning group x block interaction . Recall groupwas significant only for the number of sequence errors. None of theanalyses delivered a significant three-way interaction.Mixed ANOVAs  were conductedto reveal whether a benefit of sequential scanning of empty targetlocations could be found with the prolonged recall phase (10 blocks).The analysis for click precision did only reveal a significant blockeffect ,independent of whether or not participants moved their eyes during therecall phase.Although there were no significant effects of recall group and mostlyno significant interactions with it, numerically, the free-recall groupwas slightly better in terms of time, errors, and cursor-paths duringlater blocks . Indeed, the ANOVAs per block, which wereconducted in order to get insights into the learning-group effects (seeabove), revealed some significant recall-group effects  with better performance of thefree-recall group late during the recall phase. Additionally,significant recall group x block interactions were found forcursor-paths length in blocks 18 and 20 with significantly betterperformance of the group that learned with central fixation, but wasthen allowed to use the eyes freely  compared to all othergroup combinations that did not differ significantly from each other.These effects need to be interpreted with caution because the three-wayANOVAs did only reveal a significant recall group x block effect forprecision errors.ps < .001; error:ps < .001; sequence errors: p = .06for the FixFree and ps < .05 for all othercombinations; precision errors: ps < .05; path:ps < .001).The block effects of the three-way ANOVAs for time, errors, and pathswere again due to improvement over the course of learning, indicated bylinear trends  with a mean ageof 25 years completed Experiment 4.Apparatus, stimuli, and procedure differed from Experiment 2 in twoaspects. Firstly, a new configuration of target positions was generated with the same prerequisites as in Experiment 1.Secondly, no numbers were presented in the target circles during thevisual phase, so that the participant had to learn the clicking sequencefrom trial and error with the help of the auditory feedback. In thisway, no numbers can be used as external memory, so that also thefree-gaze participants need to encode the required clicking sequenceexplicitly early during the acquisition phase in order to perform well.This manipulation was introduced in order to prevent any benefit fromlearning with central fixation for later memory-based clicking, so thata possible benefit of looking-at-nothing can be uncovered.Analyses were the same as in the previous experiments.Participants of the fix-gaze group made on average 0.6 fixationsoutside of the fixation region per trial. Participants of the free-gazegroup performed on average 6.4 guiding fixations per trial. The mixedANOVAs (learning group x block) revealed significant block main effectsfor all performance measures, significant group main effects for trialcompletion time, number of all and precision errors, click precision,and cursor-path length, but not sequence errors and a significantinteraction for completion time, sequence errors, and precision errors(Table 7).ps < .001, Figure 8).Participants who were allowed to move their eyes freely mostly completeda trial significantly faster, with shorter cursor-paths, higher clickprecision, and fewer precision errors than participants who had to keepcentral fixation  as confirmed by significant independentsample t-tests per block. Specifically, completion timediffered significantly between groups in all blocks except block 2(t(71.58) = 1.75, zCohen’sd = 0.39, p = .08, but Wilcoxonsigned rank test W = 448.5, p <.001), number of all errors in blocks 3-10 , precision errors in all blocks,cursor-path length in blocks 3 and 5-10, and click precision in allblocks. Importantly, this time groups did not differ significantly interms of sequence errors in any of the visual blocks and the free-gazegroup produced a reasonable amount of sequence errors early duringlearning, indicating that all participants learned from trial anderror.As in the previous experiments, performance in terms of trialcompletion time, cursor-path length, and errors of all types improvedsignificantly over the course of learning, while click precisiondecreased . However, Friedman test indicateda group effect (2Χ(3) = 5,p < .05), but Wilcoxon signed rank test did notreveal any group differences per block.On average 0.4 fixations were observed outside of the fixation regionper fix-gaze participant and blank-screen recall trial. The mixed ANOVA(learning group x block) did only reveal a significant block effect(Table 8) due to a significant improvement in keeping central fixationover the course of the blank-screen phase . Friedman test indicated a group effect(2Χ(3) = 5, p < .05),presumably due to the fact that only the free-learning group contributedsignificantly to the overall linear trend (linear trendp < .05 vs. p = .38).Participants who were allowed to move their eyes freely executed onaverage 3.3 guiding fixations per recall trial. The mixed-design ANOVA(learning group x block) revealed only a significant block effect (Table8), due to a slightly decreasing number of guiding fixations over thecourse of the blank-screen phase  forthe performance measures revealed significant block effects for allperformance measures except for click precision. In addition, there wasa significant recall-group effect and a three-way interaction for trialcompletion time, a significant three-way interaction for sequenceerrors, and a significant interaction of recall group x block forprecision errors . Importantly, none of the learninggroup main effects reached significance, nor any learning group x block,nor any learning group x recall group interaction (Table 8).p = .05 for free learning with fix recall andps < .01 for all other group combinations,p < .01 fix recall group; all errors:p < .001; sequence errors: p = .10for free learning with fix recall and ps < .05 forall other group combinations; precision errors: p <.001 for free recall and p = .05 for fix recall; path:p < .001).The block effects were due to improving performance over the courseof the recall phase, indicated by significant linear trends  = 5.22, 2η = .06,p < .05) and block 15  =6.55, 2η = .08, p <.05), independent of how they learned . No other effect reached significance in the block ANOVAs.The recall group effect indicated faster clicking on the empty targetlocations by those participants who were allowed to move their eyesfreely compared to participants who had to keep central fixation . ANOVAs per block revealed that the recall groups differedsignificantly in terms of completion time during block 14. Only a very small benefit of fixating empty action target locations could be found late during the blank-screen recall phase. Therefore, looking-at-nothing might have a small facilitating effect on memory recall after a familiarization period with the blank-screen task, provided that the “fixation-learning” benefit is prevented. However, it is possible that looking-at-nothing is much more helpful for encoding non-visually marked spatial locations rather than recall of visual representations. In order to test this possibility, no visual phase was provided in Experiment 5. Instead, participants saw a blank screen from the first trial on and had to find the target locations and the correct sequence by trial and error with the help of auditory feedback. If looking-at-nothing supports encoding of non-visually marked locations, then a much stronger benefit should arise in this situation, when no visual memory representation for the target locations is present beforehand.Forty right-handed students  with a mean age of 25 years completed Experiment 5. The data of one additional participant were incomplete and did therefore not enter the analyses.Apparatus and stimuli were the same as in Experiment 3, except that no visual target information was provided throughout the experiment. A new spatial configuration of eight target regions was generated with the same prerequisites as in Experiment 1. The configuration was again the same throughout the entire experiment.However, the experimental phase consisted of 10 blocks à 10 trials blank-screen phase, only. As no visual phase preceded the blank-screen phase, participants could solve the task only by trial and error using the auditory feedback after a correct click. A “fixation-learning” benefit is thereby again prevented. If looking-at-nothing supports memory recall of previously visible information, no free-gaze benefit should arise this time. If looking-at-nothing supports memory encoding of non-visually marked locations, a much stronger free-gaze benefit should arise here, most notably during later blank blocks. If looking-at-nothing supports memory recall of locations independent of how they were learned , then a late, comparably weak looking-at-nothing benefit should arise as in the previous experiment.Analyses were the same as in the previous experiments.2Χ(3) = 10, p < .01)and sequence errors (2Χ(3) = 10,p < .01).Participants of the fix-gaze group made per trial on average 0.5fixations outside of the fixation region. Participants of the free-gazegroup performed on average 4.6 guiding fixations per trial. Mixed-designANOVAs with blank block (1-10) as within-subject factor and gaze group(free vs. fix) as between-subject factor for all performance measuresrevealed significant block effects, no group effects, and nointeractions . However, there were interactiontrends for trial completion time and click precision and significantFriedman tests for the number of all errors(ps < .001). Independent samplet-tests per block for trial completion time revealedthat the block x group interaction trend was due to significantly fasterclicking by the free-gaze group in block 8 (t(36.84) =2.17, zCohen’s d = .69, p< .5) and block 9 (t(25.68) = 2.18, zCohen’sd = .69, p < .5). There were nosignificant group differences for all other measures and blocks, neitherin parametric nor non-parametric testing.Block effects were due to improving performance over the course ofthe blank-screen phase, indicated by significant linear trends = 9.70,2η = .08, p < .01;experiment: F = 5.77,2η = .05, p < .05;F = 0.04, 2η = .00,p = .84).A between-subject ANOVA with experiment (4 vs. 5) and gaze group  on the mean trial completion time of blocks 14 and 15 of Experiment 4 and blocks 8 and 9 of Experiment 5 did not reveal a significant interaction, arguing that the size of the recall effect was not significantly different across the two experiments  orwhen the “fixation-learning” benefit was experimentally eliminated(Experiments 4 and 5). However, this benefit of looking-at-nothing wasvery small and fragile. The following discussion will elaborate whyhumans might look at target locations normally and in the absence ofvisual information.When visual information is available, important features of an actiontarget such as its orientation, surface, and size can be extracted by afixation that brings these important features on the fovea, the area onthe retina with the highest spatial resolution. Extracting thesefeatures by so-called guiding fixations cf. ,19,20,50However, here and in previous studies ,26,67,68In a precursor study , it was The precursor study  also revIn addition, the precursor study  found thIt has to be noted that covert attention can be shifted sequentiallyto the target locations in case of central fixation and these shifts ofcovert attention might already be enough for successful motor guidancecf.  for the Looking-at-nothing was originally reported in visual imagery andmemory recall tasks ,30,31,32Could looking-at-nothing support sensorimotor control by the samememory-recall mechanism? Shifting the eyes to the current targetlocation could help to recall the features of the current target such asits size and orientation and also the location of the following targetin the sequence. In this way, effector movements could be adapted forthe current sub-action, while attention could already be shifted towardsthe next target location. The results of the present study are partly inline with a memory-recall function of looking-at-nothing forsensorimotor tasks. Namely, the fact that benefits of looking-at-nothingarose late during the blank-screen phase when explicit memory should bebest, could be interpreted as support for a memory-recall function.Thus, there might be a unitary memory-recall mechanism oflooking-at-nothing for different tasks.Although there was a looking-at-nothing benefit in some experiments,this effect should not be overestimated, not only because it was verysmall and fragile, but also because keeping central fixation, thecontrol condition, might be costly in itself. Thus, alternatively,looking at remembered action-target locations might rather be aby-product of learning and automatization in highly-practicedsensorimotor sequences, while it has a real function only in tasks withmore emphasis on explicit memory recall (see cited evidence above). Aslooking-at-nothing supports recall of memorized visual details, futurestudies have to investigate whether looking-at-nothing might have morepronounced effects on sensorimotor tasks that depend on the quality ofthe memory representation of the action targets, e.g., with small-sizedtargets, high-precision instruction, or object-dependent actionselection.It is also possible that sensorimotor tasks with direct eye-handmappings, i.e., when target position of gaze and hand match, benefitmore from looking-at-nothing. The benefit from deictic codes ,38mightIn addition, it has been argued that there are similar memorybenefits of shifting the eyes and shifting covert attention toremembered locations ,74,75. TInterestingly and unexpectedly, there was a strong and robustperformance benefit during acting on remembered target locations ofparticipants who had learned the visuospatial sequence with centralfixation instead of unrestricted gaze. Why is a learned clickingsequence reproduced more accurately and faster on a blank screen, whenit was learned with central fixation rather than with unrestricted eyemovements and thus target fixations?There are in principle four possible reasons for a“fixation-learning” benefit. Firstly, it is possible that a retinotopicmap of the target configuration is acquired when having to keep centralfixation throughout the clicking trials. A retinotopic map is a mapwhere locations are coded on the basis of their position on the retina. Such anSecondly, an explanation with similar predictions is thatparticipants benefit from central fixation, because they do not have toshift the frame of reference in-between clicks. The reference for theirmovements is always the central fixation cross and their gaze positionon it. However, these explanations as well as the retinotopic mapexplanation would predict a stronger “fixation-learning” benefit forparticipants who continue central fixation than for participants whomove their eyes freely during recall. In addition, the reference frameexplanation predicts an increase in precision errors over the course oflearning only for the free-gaze group due to the accumulated error fromrealigning the frame of reference. Contrastingly, precision errorsincreased for both groups, and central fixation rather than free gazecaused more precision errors during learning (Experiments 2-4).Thirdly, participants who learn with central fixation aresignificantly slower in clicking the visual target sequence, whichprolongs their exposure to the to-be-learned sequence. Even if they canonly view the numbered circles peripherally, the extended exposure timemight give them an encoding benefit. Additionally, the fix-learningparticipants spare time for saccade planning and execution, which mightbe traded for encoding.Fourthly, when having to keep central fixation, the targets have tobe located and identified from peripheral vision. Object identificationin the periphery is restricted due to its low resolution , 63,64. The design of Experiment 4 forced also the free-gaze group to encodethe number sequence explicitly, while leaving intact all other possiblefixation advantages. Specifically, by using empty target circles, allparticipants had to find the correct clicking sequence from trial anderror on the basis of the auditory feedback. In this way, participantswere forced to explicitly encode the clicking sequence to perform thetask regardless of whether they had to keep central fixation or wereallowed to move their eyes freely. Indeed, this manipulation increasedthe number of sequence errors during learning made by the free-learninggroup to the level of the fix-learning group. More importantly, themanipulation successfully eliminated the “fixation-learning” benefitduring recall, indicating that the benefit was primarily due to enforcedexplicit encoding. The data do not support the idea that learning via aretinotopic map or the usage of a constant reference frame isbeneficial.In the present study, it was investigated whether spontaneouslooking-at-nothing behavior is beneficial for performing a sequentialsensorimotor task. In order to test this, participants could either movetheir eyes unrestrictedly or had to keep central fixation while clickinga previously learned or unknown location-sequence on a blank computerscreen. Results revealed only a very small and fragile benefit of thespontaneously performed fixations on the empty target locations. As thisbenefit of “looking-at-nothing” appeared only late during theblank-screen phase, it constitutes most likely, if any, a memory benefitrather than a benefit for motor calculation. Although there was alooking-at-nothing benefit in some experiments, this effect should notbe overestimated, not only because it was very small and fragile, butalso because keeping central fixation, the control condition, might becostly in itself. Interestingly, however, central fixation can lead to astrong benefit for sequence learning by forcing participants toexplicitly encode the target sequence rather than relying on externalvisual information.http://biblio.unibe.ch/portale/elibrary/BOP/jemr/ethics.htmland that there is no conflict of interest regarding the publication ofthis paper.The author declares that the contents of the article are in agreementwith the ethics described inThis research was supported by the Cluster of Excellence CognitiveInteraction Technology ‘CITEC’ (EXC 277) at Bielefeld University, whichis funded by the German Research Foundation (DFG).I acknowledge support for the Article Processing Charge by theDeutsche Forschungsgemeinschaft and the Open Access Publication Fund ofBielefeld University.I would like to thank Werner X. Schneider for providing helpful comments on this article.Correspondence toRebecca M. FoersterCenter for Interdisciplinary Research (ZiF) & Neuro-cognitive Psychology, Psychology Department & Cognitive Interaction Technology – Center of Excellence (CITEC)Universität BielefeldP.O. Box 10 01 3133501 BielefeldGermany++49 (0)521 106 – 4503 (Phone)++49 (0)521 106 – 156934 (Fax)rebecca.foerster@uni-bielefeld.deORCID: 0000-0002-4632-1382


Over 10 weeks, the RT group performed two sessions per week, and the mean velocity of each repetition was monitored in the leg-press and chest-press exercises at 40–65% of one-repetition maximum (1RM). The set ended when the participants reached a velocity loss of 20%. The CG maintained their daily routine. At pre- and post-test, both groups were assessed in the 1RM leg-press, 1RM chest-press, handgrip strength, medicine ball throw (MBT), walking speed, and sit-to-stand (STS). At baseline, we did not find significant differences between groups. After 10 weeks, we observed significant differences (p < 0.001–0.01) between groups in the 1RM leg-press, 1RM chest-press, MBT-1 kg, and STS. The RT group performed a total number of repetitions of 437.6 ± 66.1 in the leg-press and 296.4 ± 78.9 in the chest-press. Our results demonstrate that velocity loss effectively prescribes the volume in older adults and that a threshold of 20% improves strength-related variables in this population.We analyzed the effects of velocity-monitored resistance training (RT) with a velocity loss of 20% on strength and functional capacity in institutionalized older adults. Thirty-nine participants (78.8 ± 6.7 years) were divided into a control group (CG;  A significant challenge for public and private health services is to preserve functional capacity as people get older ,2. The pResistance training (RT) is considered an effective method to improve strength and counteract age-related declines in older adults ,5. From The literature is inconsistent and inconclusive regarding the optimal RT volume in older adults ,13. BothVelocity-monitored RT is an effective strategy for improving physical performance and controlling the training load in trained young adults ,21,22,23Therefore, the purpose of the current research was to analyze the effects of velocity-monitored RT with a velocity loss of 20% in each set on strength and functional capacity in institutionalized older adults. Considering that older individuals exhibit a degree of adaptation to RT comparable to that of younger adults due to their neuromuscular plasticity ,26,27, w10), and the time in the five-repetition sit-to-stand (STS). In session 2, we measured the handgrip strength (HGS) and the 1RM in the horizontal leg-press and seated chest-press. Following the pretests, the RT group performed a 10-week velocity-monitored RT program with two sessions per week separated by 48 h rest. The CG maintained their regular daily routine, without any form of physical exercise. In week 5 (session 10), we performed a new assessment of the 1RM in both exercises to adjust the absolute loads in the RT group [This study was a nonblinded, nonrandomized controlled trial. Forty-five older adults living in community-dwelling centers were divided into an RT group or a control group (CG), based upon their perceived availability to attend the training sessions regularly. The participants reported their availability to the institutions’ geriatricians, who then communicated their decision to our research team. After that, we divided the participants between groups. Before the pretest, all participants underwent a familiarization period of two weeks (two sessions p/week) to ensure a proper adaptation to the fitness health club facilities, coaches, and exercises. During this period, we measured the body mass, in kg,  and height, in m . We also performed a first assessment of the 1RM in the horizontal leg-press and seated chest-press exercises. After the adaptation period, we conducted two testing sessions separated by 48 h rest. In session 1, we measured the seated medicine ball throw distance with 1- (MBT-1kg) and 3-kg (MBT-3kg) medicine balls, the 10 m walking speed time (TRT group . At postn = 22) or a control group . From these, we excluded six participants due to the absence of training sessions and evaluations. Thus, 39 participants remained for the final analysis  and seated chest-press . For the leg-press, the participants had to sit on the bench (lower back in contact with the machine), bend the knees at 90°, and place the feet shoulder-width apart on the platform. On command, they had to fully extend their legs, as fast and forcefully as possible, and slowly return to the initial position. In the chest-press, the participants had to sit on the bench, abduct the shoulders, flex the elbows at 90°, grab the handles with a full grip, and maintain the wrists in a neutral position. Then, we instructed them to perform a purely concentric action, as fast and forcefully as possible, and slowly return to the initial position. In the leg-press, we controlled the eccentric phase by standing alongside the participants and placing the hands on the platform handle. In the chest-press, we were behind the participants and placed the hands on the machine’s arms to control the descending phase. The general warm-up consisted of 10 min walking on a treadmill (2–4 km/h) or pedaling on a stationary bicycle (50–70 rpm with resistance levels varying between 1–5). The specific warm-up consisted of two sets . After that, 3–5 single attempts to reach the 1RM were conceded, with a 3–5 min rest between each maximal attempt. The procedures were already described elsewhere . For theThe participants were seated on an armless chair (0.49 cm) in an erect position, with a 90° hip, knee, and elbow flexion position . They exSeated on an armless chair (0.49 cm) with the back straight and the medicine ball held in front of the chest, the participants had to throw the ball as far and fast as possible after instruction . They pe10 (T10-MV) was calculated by dividing the distance by the time (m·s−1). The CV was 2.98%, and the ICC was 0.95 (CI: 0.92–0.96).The walking speed time was recorded in an indoor wooden track. We instructed the participants to start one meter behind the starting line and finish one meter after the 10 m, to attenuate the acceleration and deceleration phases. After instruction, the participants walked over 10 m linearly as fast as possible, without running . For saf−1) and the STS mean power (STS-MP) , were calculated using the equations proposed by Alcazar et al. [The participants had to sit on an armless chair (0.49 cm) with the back straight and the arms crossed over the chest. After instruction, the participants stood up and sat down as fast as possible five times . During r et al. . The CV All training sessions were supervised by an experienced researcher and three specialist senior coaches to ensure safety and the proper execution of all the exercises. The sessions lasted 45 min and were performed in a fitness health club, at the same time (2:00–3:00 pm), with a room temperature of 22–24 °C. After a general warm-up of 10 min walking on a treadmill (2–4 km/h) or pedaling on a stationary bicycle , the participants performed the following exercises: horizontal leg-press; seated chest-press; MBT; chair squats with a weight-vest. Between sets and exercises, they rested for 2–3 min. The cool-down consisted of 5 min walking or pedaling at low intensity. For the leg-press and chest-press, the relative loads progressed from 40–65% 1RM . We verbThe MV  of each repetition was recorded in real time using a linear velocity transducer  . The T-F2,1) [t-test analyzed the differences between groups at baseline and between the variables collected during the leg-press and chest-press exercises. A mixed design 2 × 2 factorial analysis of variance (ANOVA) analyzed the differences between groups  and time  for all variables. Paired samples t-tests compared the differences within groups from pre- to post-test. A repeated-measures ANOVA (within subject-factor: time 4 levels) with post hoc Bonferroni adjustments analyzed the differences in the number of repetitions per set and the fastest MV attained against the same relative load . The percentage change was calculated with a 90% CI. The effect size (ES) between and within groups was calculated using Hedge’s g formula [p < 0.05. Statistical analyses were performed in Microsoft Office Excel®  and SPSS v26 . The data were plotted in GraphPad Prism v7 .Data are presented as mean ± SD unless otherwise indicated. The normality and homogeneity of variances were calculated and confirmed using the Shapiro–Wilk and Levene tests, respectively. The ICC (95% CI) was calculated using a two-way random effect, absolute agreement, single rater/measurement model  , while t formula . The ES  formula . The alp10-MV.At baseline, we did not observe significant differences between groups in any of the analyzed variables. Changes from pre- to post-test are presented in p < 0.001; ES = 0.83) and chest-press . The velocity loss in the leg-press was lower than in the chest-press.The repetitions per set performed in the leg-press at 55% 1RM significantly decreased from session 9 to sessions 11 and 12 . The fasWe analyzed the effects of velocity-monitored RT with a velocity loss of 20% on strength and functional capacity in institutionalized older women and men. The main finding was that a velocity loss of 20% was sufficient to increase strength and functional capacity in older adults, thus confirming our main hypothesis. Therefore, these data support velocity loss as an effective variable to prescribe the training volume in older adults. Our results also confirm our second hypothesis that performing a lower total number of repetitions than previously reported in high-velocity RT interventions with older people is enough to increase 1RM strength.Although 1RM gains have been similar and, in some cases, higher than those reported in previous high-velocity RT studies with older adults ,40,41,42Our results demonstrated that, despite the prescribed magnitude of velocity loss had been identical in both exercises, the number of repetitions per set was significantly higher in the leg-press than in the chest-press (~3 repetitions more). These data suggest that the upper muscles fatigue faster than the lower muscles in older adults when matching the same velocity loss. Our study supported this evidence by the significantly higher percentage of velocity loss observed in the chest-press than in the leg-press. These differences can be explained by the smaller muscle groups involved during upper body exercises  compared to lower body exercises . Besides, the higher presence of fast-twitch fibers in the upper musculature causes a higher degree of fatigue ,24. TherIn the leg-press, from sessions 1 to 3 we observed a significant increase in MV at 40% 1RM. Considering that an increase in MV against the same weight is an indicator of performance improvement , our parAfter 10 weeks, we did not observe any change in the HGS in the RT group. Although the HGS is a strong predictor of mortality and an indicator of general strength, its sensitivity is questionable in relation to detecting physical performance changes in older adults after RT interventions . Thus, fAt post-test, we observed significant gains in the MBT-1kg, while in the MBT-3kg we found a nonsignificant increase. These differences can be justified because only the MBT-1kg was included as part of the RT program. Indeed, when the same medicine ball weight is used both in the test and the intervention, significant gains tend to occur ,8,46. Co10, our results found a relevant aspect. In the RT group, the T10-MV increased, while in the CG it significantly decreased. These results suggest the loss of walking speed during aging and reinforce the importance for older adults to engage in RT [10 (−18% to −6%) after high-velocity RT programs with older adults [Despite the nonsignificant improvements in Tge in RT . Studiesr adults ,8,9,40. In our study, we observed significant decreases in the STS time. This result agrees with previous findings, in which significant gains from −15% to −11.8% were observed after high-velocity RT with older adults ,37,48. OThis study presents some limitations. A larger sample size would allow us to generalize the results and reduce the probability of a type II error. Moreover, an additional experimental group could give us important insights into the effects of different velocity loss thresholds on older adults’ strength and functional capacity. Including resistance exercises targeting the forearm muscles could be important to analyze their effects on the HGS. Therefore, future velocity-monitored RT interventions with older adults should include larger sample sizes, more experimental groups, and additional exercises targeting the forearm muscles.In summary, our data suggest that monitoring the velocity loss during RT is an efficient and effective strategy to prescribe the training volume in older adults and to increase muscle strength and functional capacity in this population.The current research presents a novel RT approach to prescribe the volume in older adults by monitoring each set’s velocity loss. The training method presented here opens a new possibility for coaches and clinicians to adopt an individualized intervention and optimize muscular adaptations during RT with older adults. In practical terms, two RT sessions per week with a velocity loss of 20%  and relative loads progressing from 40–65% 1RM seem to be enough to induce muscle strength adaptations and improve functional capacity in older adults aged between 70 and 90 years.


Glucocorticoids are very effective anti-inflammatory drugs and widely used for inflammatory bowel disease (IBD) patients. However, approximately 20% of IBD patients do not respond to glucocorticoids and the reason for this is largely unknown. Dietary advanced glycation endproducts (AGEs) are formed via the Maillard reaction during the thermal processing of food products and can induce a pro-inflammatory reaction in human cells. To investigate whether this pro-inflammatory response could be mitigated by glucocorticoids, human macrophage-like cells were exposed to both LPS and AGEs to induce interleukin-8 (IL8) secretion. This pro-inflammatory response was then modulated by adding pharmacological compounds interfering in different steps of the anti-inflammatory mechanism of glucocorticoids: rapamycin, quercetin, and theophylline. Additionally, intracellular reactive oxygen species (ROS) were measured and the glucocorticoid receptor phosphorylation state was assessed. The results show that AGEs induced glucocorticoid resistance, which could be mitigated by quercetin and rapamycin. No change in the phosphorylation state of the glucocorticoid receptor was observed. Additionally, intracellular ROS formation was induced by AGEs, which was mitigated by quercetin. This suggests that AGE-induced ROS is an underlying mechanism to AGE-induced glucocorticoid resistance. This study shows for the first time the phenomenon of dietary AGE-induced glucocorticoid resistance due to the formation of ROS. Our findings indicate that food products with a high inflammatory potential can induce glucocorticoid resistance; these results may be of great importance to IBD patients suffering from glucocorticoid resistance. The use of glucocorticoids is increasing due to the increased prevalence of chronic inflammatory diseases, such as asthma, rheumatoid arthritis, and inflammatory bowel diseases (IBD). Despite the effectiveness of glucocorticoids as anti-inflammatory drugs, some patients do not experience the anticipated therapeutic response and appear to be resistant to these drugs ,2,3. TheUnder physiological circumstances, glucocorticoids diffuse across the cell membrane and bind to the alpha form of the glucocorticoid receptor (GR) . After bAlthough the anti-inflammatory mechanisms of the action of glucocorticoids are well understood, it is still unknown why glucocorticoid resistance occurs in some patients. The molecular mechanisms by which glucocorticoid resistance could arise have been reviewed by Barnes . PhosphoFurthermore, ROS can damage and thereby inactivate HDAC2 which contributes to a reduced sensitivity to glucocorticoids ,16. HencRecently, a study of ours was one of the first to show that dietary advanced glycation endproducts (AGEs) cause inflammation in human macrophages in vitro by binding to the receptor for advanced glycation endproducts (RAGE) . After aTherefore, the present study was aimed at investigating the effects of dietary AGEs on the anti-inflammatory effects of cortisol in human macrophages and to unravel the underlying molecular mechanisms by which dietary AGEs induce glucocorticoid resistance in these cells.Casein from bovine milk, α-lactose monohydrate, NaOH, sodium phosphate, cortisol, theophylline, rapamycin, quercetin dihydrate, 2′,7′-dichlorofluorescin diacetate (H2DCF-DA), 2-mercaptoethanol, and thiazolyl blue tetrazolium bromide (MTT) were obtained from Sigma-Aldrich . D-glucose, glutamate, fetal bovine serum (FBS), and Dulbeccco’s Phosphate-Buffered Saline (DPBS) were obtained from Gibco . Sodium chloride, Triton-X, Tris base, LPS, and phorbol 12-myristate 13-acetate (PMA) were obtained from Sigma .Dietary AGEs were prepared as described previously . In shorEndotoxin presence in the AGE preparation was assessed by the PYROGENT Gel Clot LAL Assay with a 0.06 EU/mL sensitivity . The assay was performed in accordance to the manufacturer’s protocol using endotoxin-free dilutions and reagent tubes . Only endotoxin-free samples were used in the cell culture experiments.THP-1 monocytes  cultured in RPMI 1640 with L-glutamine, Hepes, and phenol red  supplemented with 10% (v/v) FBS, D-glucose (4.5 g/L), Na-pyruvate (1 mM), and 2-mercaptoethanol (50 μM) were seeded in a 96-well plate in a cell density of 70,000 cells/well and differentiated into macrophages by adding 200 nM of PMA to the cell culture medium and culturing them for 72 h. After differentiation, the cells were exposed to 10% (v/v) AGEs, different concentrations of LPS, cortisol, theophylline, rapamycin, and quercetin in serum and phenol red free culturing medium and always in a 50% DPBS solution. The exposure concentrations of the compounds in the AGEs were 0.16 ± 0.09 μg/mL MG-H1, 0.06 ± 0.05 μg/mL CML, 1.1 mM glucose, 20 mM lactose, and 1 g/L casein. The control conditions included phenol red free culturing medium and 50% DPBS. Ethanol (maximum 0.17 v/v%) was used as the vehicle control for cortisol, theophylline, and quercetin. DMSO (0.1 v/v%) was used as the vehicle control for rapamycin.Interleukin-8 (IL8) release in cell culture supernatant was assessed by a commercially available ELISA kit . The assay was performed in accordance with the manufacturer’s protocol.THP-1 monocytes were seeded in a 6-well plate with a cell density of 2,100,000 cells/mL and differentiated into macrophages by adding 200 nM of PMA to the cell culture medium and culturing them for 72 h. After differentiation, cells were exposed to 50% DPBS, 10% (v/v) AGEs, and 3 ng/mL LPS for 24 h. After incubation, cells were lysed with a Triton buffer consisting of 150 mM sodium chloride, 0.1% Triton-X, and 50 mM Tris pH 8, and the protein content was assessed with the BCA Assay  according to the manufacturer’s protocol.A sample of 10 µg with reducing laemmli buffer was loaded onto a 10% mini protean TGX precast gel  and run in a Bio-rad cell. Protein was transferred to an Immobilon-P PVDF Membrane . Membranes were analyzed using an Amersham Imager 600 . The primary antibodies used were Glucocorticoid Receptor (D6H2L), Phospho-Glucocorticoid Receptor (Ser211), and β-actin (13E5) from Cell Signaling Technology  and Anti-phospho-Glucocorticoid receptor Ser226 from Millipore . The secondary antibody used was Anti-rabbit IgG, HRP-linked Antibody .Intracellular ROS levels were quantified using the DCFH-DA assay. This assay measures intracellular ROS by their capacity to oxidize 2′,7′-dichlorofluorescin diacetate (H2DCF-DA) to the fluorescent DCF . In thisU test to compare two sets of treatment. The significance level was set to p < 0.05. The significance is indicated as * p < 0.05, ** p < 0.01, and *** p < 0.001.Experiments were performed in duplicate on different days using the average of the duplicates as one value. Data were analyzed using GraphPad Prism software . Western blot results were analyzed using ImageJ . Data obtained were tested for normality by using the D’Agostino and Pearson omnibus normality test. Data were analyzed with the nonparametric one-way ANOVA Kruskal–Wallis test followed by Dunn’s Multiple Comparison test or the Mann–Whitney Differentiated THP-1 cells were exposed to different concentrations of LPS and AGEs for 24 h, after which IL8 secretion was assessed by ELISA and cell viability was assessed by MTT. No cell death occurred after exposure to LPS and AGEs (data not shown). Cells were exposed to different concentrations of LPS and AGEs (data not shown) to test which concentrations of the different compounds lead to a similar IL8 secretion. Activation of mTOR can lead to GR phosphorylation and since the mTOR inhibitor rapamycin was able to reduce IL8 secretion compared to cortisol, the next step was to explore a possible role for GR phosphorylation in AGE-induced glucocorticoid resistance. The cells were exposed to either the control condition, 10% AGEs, or 3 ng/mL LPS. The protein levels of GR and phosphorylated GR were assessed by Western blot and calculated relatively to β-actin . No signAs shown in In the present study, it has been shown for the first time that dietary AGEs induced glucocorticoid resistance of macrophage-like (THP-1) cells. Exposure of THP-1 cells to dietary AGEs increased the production of IL8, indicating an inflammatory response, which was comparable to the pro-inflammatory effect of 3 ng/mL LPS. The glucocorticoid cortisol was able to decrease LPS-induced inflammation significantly better than AGE-induced inflammation. This indicates that exposure to dietary AGEs may lead to glucocorticoid resistance. Modulation of the pro-inflammatory response by different pharmacological compounds interfering in different steps of the anti-inflammatory mechanism of glucocorticoids showed that AGE-induced glucocorticoid resistance can be restored. The use of glucocorticoids is increasing due to the increased prevalence of chronic inflammatory diseases, such as asthma, rheumatoid arthritis, and IBD. Despite the effectiveness of glucocorticoids, some patients are resistant to these drugs. Glucocorticoid resistance is a disorder that is characterized by insensitivity to glucocorticoids [Barnes et al. published an excellent review about the mechanisms of glucocorticoid resistance  3]. Pho. Pho3]. Based on the results of this study and existing literature, the proposed mechanism by which AGEs induce glucocorticoid resistance is shown in In conclusion, the present study showed for the first time indications that inflammation induced by AGEs is less well mitigated by cortisol than LPS-induced inflammation. This indicates that AGEs may cause glucocorticoid resistance by increasing intracellular ROS. Quercetin is able to improve the glucocorticoid response by reducing the intracellular ROS levels.


Machine learning models to predict energy expenditure (EE) from accelerometer data have traditionally been trained on data from laboratory-based activity trials. However, accuracy is typically attenuated when implemented in free-living scenarios. Currently, no studies involving preschool children have evaluated the accuracy of EE prediction models trained on laboratory (LAB) under free-living conditions.To evaluate the accuracy of LAB EE prediction models in preschool children completing a free-living active play session. Performance was benchmarked against EE prediction models trained on free living (FL) data.25 children (mean age = 4.1±1.0 y) completed a 20-minute active play session while wearing a portable indirect calorimeter and ActiGraph GT3X+ accelerometers on their right hip and non-dominant wrist. EE was predicted using LAB models which included Random Forest (RF) and Support Vector Machine (SVM) models for the wrist, and RF and Artificial Neural Network (ANN) models for the hip. Two variations of the LAB models were evaluated; 1) an “off the shelf” model without additional training; 2) models retrained on free-living data, replicating the methodology used in the original calibration study (retrained LAB). Prediction errors were evaluated in a hold-out sample of 10 children.Root mean square error (RMSE) for the FL and retrained LAB models ranged from 0.63–0.67 kcals/min. In the hold out sample, RMSE’s for the hip LAB (0.62–0.71), retrained LAB (0.58–0.62) and FL models (0.61–0.65) were similar. For the wrist placement, FL SVM had a significantly higher RMSE  than the retrained LAB SVM  and LAB SVM . The LAB (0.64 ± 0.28), retrained LAB (0.64 ± 0.25), and FL (0.62 ± 0.26) RF exhibited comparable accuracy.Machine learning EE prediction models trained on LAB and FL data had similar accuracy under free-living conditions. Accelerometer based-motion sensors are the most widely recognized and preferred device to objectively measure physical activity in young children , 2. To dOne approach to accelerometer data reduction that has potential to significantly improve device-based measurement of physical activity and sedentary behaviour is pattern recognition or machine learning , 14. PatTo date, few investigators have implemented machine learning approaches to derive accelerometer-based EE prediction models for children under five. Due to developmental differences in movement competence and the energy cost of physical activities , accelerEE prediction models trained on features in the raw acceleration signal allows for generalizability across different monitor brands and provides greater flexibility for models to map dependencies and nonlinearities in the data , 26, 27.Although the aforementioned studies show that machine learning EE prediction models are feasible and provide accurate estimates of EE, it is important to note that the models were trained using accelerometer data from laboratory-based activity trials. Previous studies have demonstrated that machine learning models trained on laboratory-based activity trials do not perform well when deployed in free living environments , 30. TheTwenty-five children between the ages of 3–5 years (mean = 4.1 ± 1.0 y) completed a 20-minute active free play session at a location chosen by the parent or guardian. Children were recruited through a University email list-serve, local media, and word of mouth. The locations of the active free play session included the family home, community parks, and local green spaces. The research team provided age-appropriate toys and play equipment; however, children were free to engage in any activity they desired. This allowed for natural activity behaviour, transitions, and engagement with peers and the environment. The sample was evenly distributed across the age range  and comprised 5 girls and 20 boys. Prior to participation, parental written consent was obtained. The study was approved by the Queensland University of Technology’s Human Research Ethics Committee (1700000423).Fifteen children were randomly assigned to the training sample to develop and evaluate the free-living EE prediction models. The remaining 10 children served as a hold-out test sample to independently evaluate the prediction accuracy of the new free-living EE prediction models and benchmark their performance against previously published laboratory-based EE prediction models for preschool-aged children.During each active free play session, participants wore a MetaMax 3B portable indirect calorimetry unit  and an ActiGraph GT3X+ accelerometer  on their right hip and non-dominant wrist. During each session, a member of the research team used a Go-Pro Hero 5  camera to video record participants for subsequent direct observation coding of activity type.2) and carbon dioxide production (VCO2) were measured breath-by-breath using a Triple-V-Turbine, an electrochemical cell and an infrared analyser, respectively. Breath-by-breath VO2 data was averaged over a 10 s window and then smoothed using a 60 s moving average [2 was converted to units of EE  using the Weir equation [The Metamax 3B portable calorimeter was fitted using a flexible face mask and chest harness, which was appropriately tightened using adjustable straps. The device and chest harness had a total weight of 1.1 kg, making it feasible for use with young children. Following calibration according to manufacturer specifications, respiratory rate, oxygen consumption  EE prediction models: 1) data pre-processing and feature extraction, 2) feature selection, and 3) model training and testing.th, 25th, 50th, 75th, 90th), skewness, kurtosis, maximum, minimum, peak-to-peak, median crossings, zero crossings, sum, mean absolute deviation, power, lag-1 autocorrelation, log energy, inter-quartile range, variance, active samples, number of activations, mean activation interval duration, activation interval duration variability, cross-axis correlations, dominant frequency (0.25 to 5.0 Hz), and dominant magnitude (0.25 to 5.0 Hz). In addition, mean orientation angles for tilt, roll, and pitch were calculated. The resultant feature dataset was synchronized with the measured EE values by aligning date-time stamps.The tri-axial accelerometer signal was converted into a single-dimension vector magnitude (VM) and segmented into 10s non-overlapping sliding windows. Time and frequency domain features found to have utility in previous studies , 36 wereMinimum Redundancy Maximum Relevance (mRMR) feature selection was used to identify features with high discriminative ability . MinimumTo replicate the methodology used in previously published laboratory-based (LAB) EE models for preschool-age children, the supervised learning algorithms used in this study were RF and ANN for the hip; and RF and SVM for the wrist. The models were implemented using the “randomForest” , “kernlaThe accuracy of the newly derived free-living EE prediction models were compared to the LAB Hip RF and Wrist RF EE prediction models developed by Steenbock et al. , and theDifferences in RMSE statistics between the LAB, retrained LAB, and FL models were tested for statistical significance using a one-way repeated measures ANOVA. Statistical significance was set at an alpha level of 0.05. When the ANOVA F-ratio was significant, the Fisher LSD procedure was used to determine the locations of significant pairwise differences. Additionally, Bland-Altman plots were created to examine mean bias and 95% limits of agreement (LOA) for total EE expended during the free-living activity session.On average, participants expended 33.7 ± 12.2 kcals during the free play session. Based on the direct observation coding of the video files, participants completed a wide range of activities, engaging in energetic play, walking, and running for an average of 11.7 ± 4.0, 2.8 ± 1.9, and 2.4 ± 1.1 min, respectively. The average duration of seated activities was 4.3 ± 2.3 min.https://github.com/MA-QUT/Preschool_EE_Models_PLOS_One.The FL prediction models with the best LOSO-CV performance were tested in the hold-out sample. The best performing Hip RF model had 20 features, while the best performing Hip ANN model had 15 features. The best performing Wrist RF and Wrist SVM models both had 15 features. The features selected for the FL models are reported in RMSE and MAPE statistics for the FL and retrained LAB models are reported in 5,45 = 0.85, p = 0.53).RMSE and MAPE statistics for the off the shelf LAB, retrained LAB, and FL models in the hold-out sample are displayed in Figs 5,45 = 2.36, p = .05). Post-hoc analysis showed that the RMSE for the FL SVM model  was significantly higher than the off the shelf LAB SVM  and retrained LAB SVM models . No other significant differences were detected.For the wrist placement , RMSE’s , the mean bias was not significantly different from zero and predicted EE estimates were within ± 6% of directly measured EE. However, the 95% LOA’s were wide for all models. Based on the line of best fit, the lower and upper prediction limits for an individual ranged from -92.6% to 6.8% at 10 kcals, -55.8% to 33.5% at 30 kcals, and -26.5% to 67.6% at 50 kcals.Bland-Altman plots depicting the agreement between predicted and observed total EE during the free play session are displayed in Figs For the wrist placement , all sixMachine learning models for predicting EE in young children have been shown to accurate when evaluated under laboratory conditions. However, to advance the application of machine learning methods for the assessment of physical activity, EE prediction models trained on data from laboratory-based activity trials need to be evaluated in independent samples under true free-living conditions. To our knowledge, this is the first study to evaluate the accuracy of machine learning EE prediction models for preschool-aged children under true free-living conditions. Furthermore, this is the first study to benchmark the performance of EE prediction models trained on free-living data against models trained on laboratory-based activity trials. The results indicate that EE prediction models trained on laboratory data exhibit comparable accuracy to models trained on free living data under free-living conditions. The laboratory trained models exhibited RMSE’s within 0.10 kcals/min of the free-living models. Moreover, RMSE’s for the hip and wrist laboratory-trained models were within 0.07 kcals/min and 0.03 kcals/min of their laboratory cross-validation performance, respectively.Our finding that laboratory-based models exhibit similar prediction accuracy to models trained on free-living data was unexpected and contrary to the results reported in studies evaluating laboratory trained activity classification models under free-living conditions , 30. TheWith the exception of the LAB Hip RF model, all of the EE prediction models provided acceptable group level estimates of total EE within ± 6% of measured EE. However, the prediction limits for an individual were wide and all models exhibited strong evidence of proportional bias in which EE was overestimated for play sessions with low total EE and underestimated for play sessions with high total EE. The systematically larger prediction errors observed for play sessions with low and high total EE is difficult to explain. However, given the relatively short duration of the free play sessions, and the pulsatile nature of young children’s movement behaviours, there may have been insufficient training instances with low and high physical activity intensity for the models to make accurate predictions across the full physical activity intensity continuum. Notably, the laboratory-based and free-living models were trained on data where 75% of the directly measured EE values were between 1.4 to 2.5 kcals/min (2.2 to 3.8 METs). Therefore, it is possible that the models were less than adequately fitted to provide accurate EE predictions outside this range. As a result, EE for a play session tended to be overestimated if physical activity intensity was predominantly low and underestimated if physical activity intensity was mostly high see . To incrThe current study had several strengths. Specifically, all models were evaluated in an independent hold out sample of children engaging in unconstrained activities in a variety of locations. Additionally, prediction models for both the hip and wrist were evaluated. The hip and wrist are the most common wear locations for accelerometers . TherefoOffsetting these strengths were several study limitations. First, due to the demands of the data collection protocol, the active play sessions were restricted to 20 minutes. Although participants engaged in a broad range of physical activity behaviours typically performed by preschool-aged children, longer play sessions or multiple play sessions for each participant would have provided the opportunity for more free-living active play evaluations in different environmental contexts which may have allowed for participants to engage in more activities at the low and high end of the physical activity intensity continuum. Second, the study had a relatively small training and hold-out sample. However, the sample size of 25 children provided adequate data to both train and test the machine learning models. With 15 children, the 20-minute free living play sessions generated 1,578,000 data points providing 1,578 10-second windows to train models. With 10 children in the hold-out set, there were 974,000 data points providing 974 10-second windows to test the models. Third, the free-living models did not use lag and lead features from adjacent windows. This would have provided more information for model predictions and/or accounted for the dependence between adjacent windows. However, in order to make direct comparisons with the laboratory-trained models, the free-living models used the same feature sets and did not include information from adjacent windows. Fourth, the study participants were healthy typically developing children. As such, the models evaluated are not generalizable to clinical populations with movement impairments or elevated energy cost of locomotion.In summary, when evaluated under true free-living conditions, laboratory-trained accelerometer EE prediction models for preschool-aged children exhibited similar accuracy to models trained on free-living data. Although the laboratory-based models generalized well to a free-living environment and exhibited acceptable accuracy at the group level of measurement, the strong evidence of proportional bias and wide prediction limits exhibited by all the models suggests that they may be inappropriate for predicting EE in individuals. To improve predictive accuracy, future studies should train models using accelerometer data with enough training instances of physical activity with low and high EE for accurate prediction over the complete physical activity intensity continuum. In addition, the inclusion of physiological sensor data such as heart rate or person-level features such as height and weight may improve accuracy.S1 FigOrange indicates observed EE between 1.4 and 2.5 kcals/min; Blue indicates observed EE <1.4 and >2.5 kcals/min.(DOCX)Click here for additional data file.


To evaluate the association between the percentages of older age groups among confirmed SARS-CoV-2 infections and the country-specific case fatality rate (CFR).This ecological study analyzed data from the 20 most severely affected European countries, USA and Canada, in which national health authorities provided data on age distribution and gender among confirmed SARS-CoV-2 cases and deaths.R2 = 0.803 for all countries, R2 = 0.961 after exclusion of three countries with incongruent data). Each 5% point increase of this older age group among confirmed SARS-CoV-2 cases was associated with an increase in CFR of 2.5% points (95% CI 1.9–3.1).The proportion of individuals older than 70 years among confirmed SARS-CoV-2 cases differed markedly between the countries, ranging from 4.9 to 40.4%. There was a strong linear association between the proportion of individuals older than 75 years and the country-specific CFRs (Data from 20 European countries and the USA and Canada showed that the variance of crude CFR of COVID-19 is predominantly (80–96%) determined by the proportion of older individuals who are diagnosed with SARS-CoV-2. The age distribution of SARS-CoV-2 infections is still far from being homogeneous. Detailed demographic data have to be taken into account in all the analyses on COVID-19-associated mortality. We urgently call for standardized data collection by national health authorities.The online version of this article (10.1007/s15010-020-01538-w) contains supplementary material, which is available to authorized users. In the current pandemic, the country-specific crude case fatality rates (CFRs), the percentage of COVID-19-associated deaths among confirmed SARS-CoV-2 infections, have been the subject of much speculation. Although it became quickly clear that older age is a major risk factor for mortality , 4, manyThese factors include not only differences in the overall age structure of the general population of a country and coresidence patterns , but alsMost articles have considered that older age plays a vital role in influencing severe disease and negative clinical outcomes. However, to clarify the association of older age groups diagnosed with SARS-CoV-2 infection with the country-specific CFR, we have collected available age- and gender-specific data provided from national health authority websites of the 20 most severely affected European countries and of USA/Canada.For Europe, we evaluated the 20 most severely affected larger countries, as defined by numbers per million inhabitants and with at least 1000 confirmed infections. Country-specific confirmed case and death numbers to estimate the country-specific CFR, as well as all available data on age distribution of confirmed cases and deaths were extracted from national health authority websites of European countries and Canada by July 6, 2020 (see supplement). Age-specific data were available for Belgium, Denmark, Germany, Iceland, Ireland, Italy, Moldova, Netherlands, Portugal, Spain, Sweden, Switzerland, and the United Kingdom. For France, only a limited number of cases reported to center laboratories were available. Six countries did not report age-specific data  and were replaced by the countries ranking next in order of numbers, namely Austria, Norway, Romania, Estonia, Finland and the Czech Republic. For the USA, we used a detailed report on 1,320,488 laboratory-confirmed COVID-19 cases individually reported to Centers of Disease Control .The numbers of confirmed SARS-CoV-2 cases and deaths were collected as well as the corresponding reported age strata and gender distribution. In countries where only age groups of 65–74 years were available for confirmed cases (see Supplement), we assigned 50% to the group over 70 years. We also performed sensitivity analyses by assigning not only 50% but also 33% and 67% of cases to the younger age groups in countries using different age strata for reporting confirmed SARS-CoV-2 cases (resulting in overlapping strata). For the age group of over 70 years, the percentage of this age group among deaths was assessed. For the association between the country-specific percentage of age group over 70 and age group over 75 years among confirmed infections and the country-specific CFRs, linear regression analyses were performed. Countries were weighed according to their total numbers of COVID-19-associated deaths.I2 as measure of consistency/heterogeneity) were estimated using a random effects model applying the method of DerSimonian and Laird. Statistical analyses were performed using Stata/SE 15.1, StataCorp LLC, USA.In countries in which age strata and gender distributions were available for both cases and deaths, we estimated the age-stratified CFRs, dividing the absolute death numbers by the cases in a given age group, separately for women and men. Given the heterogeneous reporting in most countries, only three age groups were used, namely < 60 years, 60–79 years and > 80 years. The overall age-specific case fatality risk ratios across countries for men compared to woman  and At the time of data-cut , a total of about 3.36 million confirmed SARS-CoV-2 cases and 283,792 associated deaths had been documented for the 22 countries, yielding an overall CFR of 7.28%. As shown in Table The data extent and quality in reporting age- and gender-specific data varied considerably between the countries (supplement). Only a few countries reported different age groups for both cases and deaths stratified by gender. In total, roughly 2.63 million confirmed SARS-CoV-2 cases with available age data could be included in the analysis .Except for Austria, male patients accounted for less than half of the confirmed diagnoses, with the lowest rates of male patients in France, Belgium and the Netherlands. In contrast, male patients accounted for more than 50% among SARS-CoV-2-associated deaths in most countries. Except for Norway and Finland, in all the countries, male patients had a higher crude mortality risk with a risk ratio varying between 1.08 (Canada) and 2.01 (Netherlands).The proportions of patients older than 70 years among confirmed SARS-CoV-2 infections differed markedly between countries, ranging from 4.9 (Iceland) to 40.4% (United Kingdom).As shown in Table R2 values were 0.803 (0.766), explaining 80.3% (76.6%) of the variance in the country-specific CFR rates. Each 5% point increase of individuals above 75 years among SARS-CoV-2 cases was associated with an increase in overall CFR of 2.5% points (95% CI 1.9–3.1% points). Similar results were seen when adjusting for gender or when an unweighted linear regression was performed (data not shown). Results remained also stable when in countries using different age strata for reporting confirmed SARS-CoV-2 cases (resulting in overlapping strata), 33% or 67%  of cases were assigned to the younger age group.There was a strong association between the proportion of older patients and the country-specific CFRs in our linear regression model Fig. . For theR2 values were 0.868 and 0.856 for patients older than 75 and 70 years of age, respectively. If UK is also excluded , R2 values were 0.961 and 0.960, respectively.When France (which had a very low rate of infections with known age) and Spain  were excluded, We have also applied our model to other countries outside Europe that have provided detailed data on age distribution among confirmed SARS-CoV-2 cases such as Australia, Singapore, Japan, China, Republic of Korea or Brazil, yielding very similar results (data not shown).I2 = 82%), 1.73  and 1.50 , respectively. Of note, country-specific CFRs were all significantly higher for men in the three age groups apart from Moldova and Norway in the age group > 80 years .In Fig. The striking differences in country-specific case fatality rates observed throughout the world have prompted a huge debate and speculations about the reasons. A myriad of heterogeneous factors contributing to country-specific differences has been evaluated, suggested and proposed during recent months, including genetic, viral, medical, socioeconomic and environmental issues –21.The present analysis of 20 European severely affected countries, USA and Canada strongly indicates that the crude CFR of COVID-19 is predominantly determined by the proportion of patients aged older than 75 years of all individuals diagnosed with SARS-CoV-2 infection. Our simple regression model explained more than 80% of the variance between the countries. Of note, our intention was neither to apply complex mathematical models nor to question the role of other factors contributing to variations in mortality. However, when these factors are discussed and evaluated, detailed information on age distribution among the confirmed SARS-CoV-2 cases have to be taken into account. This is all the more important when countries report relatively low CFRs. In the case of Germany, Russia or India, for example, many explanations have been put forward for the relatively low mortality rates in these countries and have been subject to speculations in the media , 24. RepOur data confirm and extend the findings of a recently published early analysis of data reported until April 19, 2020 in which age-standardization of CRFs reduced their variation (standard deviation) by 66% across nine countries .Our data also show that, even in a phase of declining infection numbers (in Europe and Canada), there are still dramatic differences in the proportion of older age groups among confirmed SARS-CoV-2 cases, ranging from 4.9 to 40.4% in European countries. Although we did not have information on testing policies or on different reporting systems of cases and deaths, the present data indicate marked differences between the age distribution of the SARS-CoV-2 epidemic. In many European countries, the distribution of SARS-CoV-2 infections in the general population is still far from homogeneous. We speculate that the marked variation of CFR across countries could diminish over time, especially if less affected countries fail to protect their older age groups. However, the variation of the CFR in specific age groups as shown in Fig. In the countries with available data on gender and age groups in both cases and deaths, we found marked differences between countries and between women and men. In almost all countries, the CFR for men was markedly higher in all age groups, especially in younger age groups. The particularly high rates in the UK may be in part explained by the two-phase official testing programme. Due to limited testing capacity, during the first phase, the strategy was to perform confirmatory SARS-CoV-2 testing only in hospitals and for those with a medical need. Around 66% of the confirmed cases in the UK were derived from the first phase, probably overestimating CFR there.There is no doubt that our study has several limitations. We could not analyse the age groups more in detail and did not stratify age groups for gender as not many countries reported gender-specific age distribution in both cases and deaths. For some larger countries such as Turkey or France, data on ages were completely missing or incomplete. Many other factors were neglected, such as testing policies and other important health care issues.However, our simple analysis of 20 European countries and USA and Canada strongly indicates that the crude CFR of COVID-19 appears to be predominantly determined by the proportion of patients older than 70–75 years of age who are diagnosed with SARS-CoV-2. We urgently call for standardized data collection by national health authorities. The numbers of confirmed cases and deaths should be precisely stratified by gender and age groups, preferably grouped at 5-year intervals for all subgroups.Supplementary file1 (DOCX 32 kb)Below is the link to the electronic supplementary material.